1. Field
This application relates generally to sales automation and forecasting systems and, more particularly, to tracking and storing sales data for performance prediction.
2. Background
Sales automation systems are common in the art. In standard usage, these systems aid a sales representative, a sales manager, or both, to be more efficient at selling a product or service. Sales forecasting analytics are commonly a component of a sales automation systems, and are largely focused towards the sales manager to help forecast and manage the sales process.
A sales automation system is generally a tool that allows sales representatives and managers to organize contact records, as well as manage records associated with sales quotes and invoices. It can also work in the context of a ‘Sales Methodology’ where the sales process is structured around the representative working though a set of stages in an attempt to complete a sale. Sales automation tools typically allow the tracking of such records in terms of time periods associated with fiscal or financial accounting. During these ‘sale periods’ it is important for sales representatives and managers to have analytical reports defining progress towards various goals.
Typically the sales representative recognizes the benefit of using a sales automation system for maintaining their list of contacts, but most other tasks are considered overhead with little benefit. Thus, sales automation systems commonly suffer from a lack of full acceptance by the sales staff, which limits their usefulness to the sales managers as well—if the sales representative doesn't utilize the system then the manager does not have a full picture of the sales process. As the sales representative is responsible for using the system to track individual deal progress through the sales stage, should they fail to log the sales progress the system is left without the valuable data necessary for the sales manager to forecast and manage the sales goals. A system that provides incentive to a sales representative to use features beyond a contact management system is needed. Additionally, in part because of the lack of sales data and in part because of the prior state of the art, many analytics used by the sales manager are quite simple models with manual parameters based upon the sales manager's intuitions about prior performance.
Many algorithms in the general field of data mining provide resources to a knowledgeable individual for extracting relevant information from large amounts of data. There exist data mining applications to aid in this process. In some instances there are data mining approaches incorporated into other systems, such as sales automation systems. These incorporated techniques are usually quite rudimentary compared to the full suite of techniques available in a complete data mining system, yet still require some level of sophistication on the part of the user (in sales automation systems the user would be the sales manager). Some of the more advanced techniques available would be standard statistical approaches for assigning error bars or applying a linear regression analysis. These statistical approaches are often guided by or overridden with ad-hoc scaling factors based upon the sales manager's intuition, such as: “Bob usually over promises his amount sold by about 25%, yet Sue is more conservative and usually under promises by 15%. Therefore I will adjust Bob's sales predictions down by 25%, but increase Sue's predictions by 15%.”
There exist new data mining and machine learning techniques which can go beyond the traditional analyses, above, but they require data to work accurately to overcome the ad-hoc manual scaling factors. To collect this data the sales representative must be motivated to provide the information. Stereotypically, sales people are motivated by two goals: meeting personal monetary targets, and out performing their peers. Methods that target these motivations are needed and will increase the acceptance of a sales automation system by the sales people, and hence provide a richer set of useful data to the sales manager. Most prior systems have failed to adequately provide features found compelling to the sales representative, and have overlooked the connection that the sales manager's job is best done with the full data available from an engaged sales representative. Some prior systems have recognized the benefits of catering to the interests of a sales representative, but have neglected to use the data naturally collected by the sales automation system to reflect back and help provide the necessary feedback to keep the system accurately tuned—commonly the systems relied on a manual configuration of the various parameters.
A better sales automation and forecasting management system is needed to address the above noted shortcomings in the prior art.